Peri-luminal COROnary CTa AI-driven radiOMICS to Identify Vulnerable Patients (CORO-CTAIOMICS)

Coronary Artery Disease Clinical Trial

— CORO-CTAIOMICS  

Official title:

Peri-luminal COROnary CTa AI-driven radiOMICS to Identify Vulnerable Patients

Clinical Trial Details — Status: Recruiting

Administrative data

• NCT number: NCT06029777
• Other study ID #: PNRR-MAD-2022-12376633
• Status: Recruiting
• Start date: July 31, 2023
• Est. completion date: May 20, 2025

Study information

• Verified date: September 2023
• Source: IRCCS San Raffaele
• Contact: Antonio Esposito
• Phone: 02 2643 6102
• Email: esposito.antonio[@]hsr.it
• Is FDA regulated: No
• Study type: Observational

Clinical Trial Summary

CAD is a leading cause of mortality in Europe. cCTA is recommended to rule out obstructive CAD, but, in most patients, it shows non-obstructive CAD. The management of these patients is unclear due to lack of reproducible quantitative measurement, beyond stenosis severity, capable to assess the risk of disease progression towards developing MACEs. To improve identification and phenotypization of patients at high risk of disease progression, we propose the application of artificial intelligence algorithms to cCTA images to automatically extract periluminal radiomics features to characterize the atherosclerotic process. By leveraging machine-learning empowered radiomics we aim to improve patients' risk stratification in a robust, quantitative and reproducible fashion. By developing a novel quantitative AI based cCTA measure, we expect to provide a risk score capable to identify patients who can benefit of a more aggressive medical treatment and management, thus improving outcome

Description:

Background and rationale

In the last years, cCTA has become a pivotal diagnostic tool in the setting of suspected CAD. The latest ESC guidelines recommend cCTA as the first line diagnostic test for patients with symptoms suspected to originate from CAD, especially in intermediate pre-test probability (15 to 85%). patients. The AHA and Italian guidelines essentially suggest the same approach. The recommendations are based on the extremely high accuracy of cCTA in ruling-out obstructive CAD. As result we assist to a tremendous increase in the number of patients undergoing CCTA in the daily clinical routine. Most of these patients (80%) result to not have obstructive CAD, but this result does not mean that all this patients are at low risk. The prognostic stratification of these patients is still an urgent unmet need. Among patients with absence of obstructive CAD we found patients with different degree of atherosclerotic burden and patients with different kind of plaques (high or low risk plaques) and different degrees of coronary wall and pericoronary inflammation. The prognostic stratification of these patients is still an urgent unmet need. The cCTA images include a lot of information with a great potential informative content about the atherosclerotic burden and the vulnerability of the non-obstructive CAD of each single patients, but all these information are not currently exploited in the clinical routine to change the patients management for a lack of robust and reproducible tools to extract this data in quantitative way and o integrate them in a prognostic risk score.

In the last years, it has been shown that coronary artery plaque characteristics (e.g. lesion length, volume, stenosis, attenuation, remodeling index, etc) (2) and pericoronary adipose tissue (3) attenuation carry a significant prognostic value. However, it is known that many of these biomarkers suffer from low reproducibility, mainly due to technical constrains in automatic or semiautomatic separation of plaques from surrounding adipose tissue. This may cause the incorrect inclusion of plaques into the segmentation of pericoronary adipose tissue and vice-versa, leading to unreliable results in the assessment of plaque burden and plaque attenuation (4). Furthermore, the qualitative or semiquantitative evaluation of the aforementioned plaque characteristics and pericoronary fat density might reflect only part of the information available. In this scenario, radiomics-based assessment may unveil information hidden to the human eye, leading to better risk stratification of cardiovascular risk (5, 6).

Our study adds two major improvements in prognostic risk stratification based on cCTA plaque and adipose tissue analysis. First, we propose a method to segment both the plaques and the pericoronary adipose tissue easily and independently by semiautomatic or automatic identification of the edge between plaque and fat (which is critical). In fact, our tool will perform a segmentation of all the tissue included in a circular range outside the coronary lumen with a diameter based on the lumen diameter itself. This technical solution will greatly increase reproducibility of segmentation. Secondly, we propose the use radiomics to analyze this circular pericoronary milieu, potentially individuating novel biomarkers -invisible to human eye- of CAD instability capable of providing important prognostic value.

In detail, the novelty of our research lies in the fact that 1) pericoronary adipose tissue and plaques will be treated as a single milieu, thus significantly reducing the effort for accurate tissue segmentation and reducing reproducibility concerns; 2) radiomics will be applied to extract meaningful information invisible to human eye capable. Furthermore, this radiomics approach will be supported by machine learning (ML) models including regularized regression, genetic algorithms and deep learning, due to the capability of ML to directly manage and assess the huge amount of data extracted from the radiological images. Thus, the final outcome of our research will be an algorithm capable of predicting the risk of MACEs of the single patient by automatically analyzing peri-luminal coronary tissue radiomics data derived from cCTA performed in the routine clinical practice.

Recruitment information / eligibility

• Status: Recruiting
• Enrollment: 2190
• Est. completion date: May 20, 2025
• Est. primary completion date: May 20, 2024
• Accepts healthy volunteers: No
• Gender: All
• Age group: 18 Years and older

Eligibility

Inclusion Criteria:

1. Patients with CT performed for CAD assessment between 2017 and 2019.

2. Follow-up duration of at least 4 years.

Exclusion Criteria:

1. Refusal to participate in the study

2. Age <18 years old

3. History of previous coronary revascularization

4. Presence of other cardiovascular comorbidities (e.g. inflammatory cardiomyopathy, valvular cardiomyopathy, idiopathic dilated cardiomyopathy, infiltrative cardiomyopathy)

Related Conditions & MeSH terms

• Coronary Artery Disease
• Coronary Disease
• Myocardial Ischemia

Locations

Country	Name	City	State
Italy	IRCCS San Raffaele	Milano

Sponsors (2)

Lead Sponsor	Collaborator
IRCCS San Raffaele	Ministry of Health, Italy

Country where clinical trial is conducted

Italy, 

References & Publications (13)

Bardosi ZR, Dejaco D, Santer M, Kloppenburg M, Mangesius S, Widmann G, Ganswindt U, Rumpold G, Riechelmann H, Freysinger W. Benchmarking Eliminative Radiomic Feature Selection for Head and Neck Lymph Node Classification. Cancers (Basel). 2022 Jan 18;14(3):477. doi: 10.3390/cancers14030477. — View Citation

Brown PJ, Zhong J, Frood R, Currie S, Gilbert A, Appelt AL, Sebag-Montefiore D, Scarsbrook A. Prediction of outcome in anal squamous cell carcinoma using radiomic feature analysis of pre-treatment FDG PET-CT. Eur J Nucl Med Mol Imaging. 2019 Dec;46(13):2790-2799. doi: 10.1007/s00259-019-04495-1. Epub 2019 Sep 4. — View Citation

Cho HH, Lee HY, Kim E, Lee G, Kim J, Kwon J, Park H. Radiomics-guided deep neural networks stratify lung adenocarcinoma prognosis from CT scans. Commun Biol. 2021 Nov 12;4(1):1286. doi: 10.1038/s42003-021-02814-7. — View Citation

Emerson RW, Adams C, Nishino T, Hazlett HC, Wolff JJ, Zwaigenbaum L, Constantino JN, Shen MD, Swanson MR, Elison JT, Kandala S, Estes AM, Botteron KN, Collins L, Dager SR, Evans AC, Gerig G, Gu H, McKinstry RC, Paterson S, Schultz RT, Styner M; IBIS Network; Schlaggar BL, Pruett JR Jr, Piven J. Functional neuroimaging of high-risk 6-month-old infants predicts a diagnosis of autism at 24 months of age. Sci Transl Med. 2017 Jun 7;9(393):eaag2882. doi: 10.1126/scitranslmed.aag2882. — View Citation

Goeller M, Achenbach S, Herrmann N, Bittner DO, Kilian T, Dey D, Raaz-Schrauder D, Marwan M. Pericoronary adipose tissue CT attenuation and its association with serum levels of atherosclerosis-relevant inflammatory mediators, coronary calcification and major adverse cardiac events. J Cardiovasc Comput Tomogr. 2021 Sep-Oct;15(5):449-454. doi: 10.1016/j.jcct.2021.03.005. Epub 2021 Apr 3. — View Citation

Kolossvary M, Karady J, Szilveszter B, Kitslaar P, Hoffmann U, Merkely B, Maurovich-Horvat P. Radiomic Features Are Superior to Conventional Quantitative Computed Tomographic Metrics to Identify Coronary Plaques With Napkin-Ring Sign. Circ Cardiovasc Imaging. 2017 Dec;10(12):e006843. doi: 10.1161/CIRCIMAGING.117.006843. — View Citation

Lin A, Kolossvary M, Yuvaraj J, Cadet S, McElhinney PA, Jiang C, Nerlekar N, Nicholls SJ, Slomka PJ, Maurovich-Horvat P, Wong DTL, Dey D. Myocardial Infarction Associates With a Distinct Pericoronary Adipose Tissue Radiomic Phenotype: A Prospective Case-Control Study. JACC Cardiovasc Imaging. 2020 Nov;13(11):2371-2383. doi: 10.1016/j.jcmg.2020.06.033. Epub 2020 Aug 26. — View Citation

Nerlekar N, Ha FJ, Cheshire C, Rashid H, Cameron JD, Wong DT, Seneviratne S, Brown AJ. Computed Tomographic Coronary Angiography-Derived Plaque Characteristics Predict Major Adverse Cardiovascular Events: A Systematic Review and Meta-Analysis. Circ Cardiovasc Imaging. 2018 Jan;11(1):e006973. doi: 10.1161/CIRCIMAGING.117.006973. — View Citation

Riley RD, Snell KI, Ensor J, Burke DL, Harrell FE Jr, Moons KG, Collins GS. Minimum sample size for developing a multivariable prediction model: PART II - binary and time-to-event outcomes. Stat Med. 2019 Mar 30;38(7):1276-1296. doi: 10.1002/sim.7992. Epub 2018 Oct 24. Erratum In: Stat Med. 2019 Dec 30;38(30):5672. — View Citation

Saito T, Rehmsmeier M. The precision-recall plot is more informative than the ROC plot when evaluating binary classifiers on imbalanced datasets. PLoS One. 2015 Mar 4;10(3):e0118432. doi: 10.1371/journal.pone.0118432. eCollection 2015. — View Citation

Tzolos E, McElhinney P, Williams MC, Cadet S, Dweck MR, Berman DS, Slomka PJ, Newby DE, Dey D. Repeatability of quantitative pericoronary adipose tissue attenuation and coronary plaque burden from coronary CT angiography. J Cardiovasc Comput Tomogr. 2021 Jan-Feb;15(1):81-84. doi: 10.1016/j.jcct.2020.03.007. Epub 2020 Apr 14. — View Citation

Varma S, Simon R. Bias in error estimation when using cross-validation for model selection. BMC Bioinformatics. 2006 Feb 23;7:91. doi: 10.1186/1471-2105-7-91. — View Citation

Vickers AJ, Elkin EB. Decision curve analysis: a novel method for evaluating prediction models. Med Decis Making. 2006 Nov-Dec;26(6):565-74. doi: 10.1177/0272989X06295361. — View Citation

* Note: There are 13 references in all — Click here to view all references

Outcome

Type	Measure	Description	Time frame	Safety issue
Primary	Composite outcome	All-cause mortality, myocardial infarction, due to unstable angina or heart hospitalization failure, late coronary revascularization	48 months from CCTA